J. Russell Hoverman, MD, PhD

To compare the costs of: 1) chemotherapy treatment across clinical, demographic, and geographic variables; and 2) various cancer care-related cost categories between patients receiving chemotherapy in a community oncology versus a hospital outpatient setting.

 Data from the calendar years 2008 to 2010 from the Truven Health Analytics MarketScan Commercial Claims and Encounters Database were analyzed. During 2010, the data set contained approximately 45 million unique commercially insured patients with 70,984 cancer patients receiving chemotherapy. These patients were assigned to cohorts depending on whether they received chemotherapy at a community oncology or hospital outpatient setting.

Cost data for 9 common cancer types were extracted from the database and analyzed on a per member per month basis to normalize costs; costs included amounts paid by the payer and patient payment. Community oncology and hospital outpatient setting chemotherapy treatment costs were categorized and examined according to cancer diagnosis, patient demographics, and geographic location.

Patients receiving chemotherapy treatment in the community oncology clinic had a 20% to 39% lower mean per member per month cost of care, depending on diagnosis, compared with those receiving chemotherapy in the hospital outpatient setting. This cost differential was consistent across cancer type, geographic location, patient age, and number of chemotherapy sessions. Various cost categories examined were also higher for those treated in the hospital outpatient setting.

The cost of care for patients receiving chemotherapy was consistently lower in the community oncology clinic compared with the hospital outpatient setting, controlling for the clinical, demographic, and geographic variables analyzed.

This study demonstrates that cost of care for chemotherapy patients is lower in the community oncology clinic than the hospital outpatient setting.

Mean per member per month cost of care, depending on diagnosis, was 20% to 39% lower for those receiving chemotherapy in a community oncology clinic compared with the hospital outpatient setting.

Cost differential was consistent across diagnosis, geography, patient age, and number of chemotherapy sessions.

As a larger proportion of oncology services are being provided in the hospital outpatient setting, policy makers and payers should be aware that shifts in sites of service may negatively impact cancer spending.

Although cancer death rates have consistently decreased because of improved early detection, prevention, and treatment, the cost of cancer care has significantly increased in conjunction with these accomplishments.

The National Institutes of Health estimated that the United States spent $89 billion on the treatment of cancer in 2007.

Given the total 2011 national health expenditure of $2.7 trillion—which is more than 10 times the $256 billion spent in 1980&mdash;cancer represents approximately 4% of all healthcare costs in the country.

 As cancer patients live longer and consume more healthcare dollars, this percentage will likely increase.

Within a system of limited resources, maximizing patient outcomes and minimizing costs are primary concerns. Chemotherapy is a common cancer treatment modality that contributes significantly to the overall high cost of cancer treatment. Within the population diagnosed with cancer, approximately 22% of patients will receive chemotherapy in a given year.

In a commercial population, cancer patients make up about 0.68% of a population but account for about 10% of the overall healthcare cost.

Chemotherapy is administered in a variety of settings, although the majority is administered as outpatient treatment.

 For outpatient sites of care, patients predominantly receive treatment at a hospital outpatient (HOP) department or community oncology clinic (COC). In recent years, market conditions have caused some medical oncologists to shift from community to hospital sites of service, and as a result, a larger proportion of oncology services are being provided in the HOP setting.

 In a review of CMS claim payments, it was noted that the share of fee-for-service chemotherapy administration procedure claims in the HOP increased considerably over time—from 13.5% in 2005, to 33% in 2011.

In a time of scarce healthcare resources and increased scrutiny around healthcare spending, how this shift in the site of care impacts oncology costs bears consideration.

The objectives of this study were to compare the costs of: 1) chemotherapy across clinical, demographic, and geographic variables; and 2) various cancer care-related cost categories between patients receiving chemotherapy in a COC setting with those receiving chemotherapy in an HOP setting.

This study is based on data culled from the Truven Health Analytics MarketScan Commercial Claims and Encounters Database, which contains eligibility, demographic, and medical and pharmacy claims data for commercially insured members (patients). During 2010, the data set contained 45,239,752 unique lives covered by commercial insurance plans. The data analyzed for this study included patient demographic, geographic, facility, physician claims, pharmacy claims, and plan eligibility information for calendar years 2008 to 2010.

To be included in the study, a patient had to first have 1 facility or 2 physician claims with an 

International Classification of Diseases, Ninth Revision, Clinical Modification

 diagnosis code for 1 of the cancer types listed in 

. All diagnosis codes (eg, primary diagnosis, secondary diagnosis) were used. Laboratory and radiology claims, however, were not used. Patients with a diagnosis of multiple cancer types were assigned to 1 type, according to the hierarchical list in Table 1. Patients were also required to have at least 1 chemotherapy drug claim, defined according to the Health Care Financing Administration Common Procedural Coding System and National Drug Codes. Study participants were identified if they met these criteria in calendar years 2008 to 2009; claims were accrued for 2008 to 2010 to allow for a larger exposure period.

Patients were excluded if they did not have cancer (n = 43,854,833); were not concurrently receiving chemotherapy (n = 1,244,899); had HIV/AIDS or an organ transplant (n = 20,937); had <2 physician cancer claims and no facility cancer claims (n = 37,804); had <90 days of insurance eligibility beginning with the first date of service (DOS) for chemotherapy (n = 874); had chemotherapy administration at both HOP and COC facilities (n = 7659); or had leukemia (these patients were excluded because of a large difference in age between cohorts) (n = 1762). We did not exclude based on age. The oldest patients we found (commercially-insured, no Medicare) were born in 1943 and the youngest were born in 2009; 2008 and 2009 were used for identification, so some patients were aged ~1 year (albeit very few).

Eligible patients were grouped into 2 cohorts depending on the site of service (ie, COC vs HOP) for their chemotherapy administration. Place of service (POS) from the claims was used to determine the setting, with a POS of 11 used to indicate claims at a physician's office, and a POS of either 22 or 95 used to indicate a claim in the outpatient setting. Patients with chemotherapy in both settings were excluded, as parsing the data would have been challenging.

Costs—the amount paid by the payer and excluding patient payment&mdash;including chemotherapy drug costs, were accrued for calendar years 2008 to 2010, beginning with the first DOS for chemotherapy. Claims with a trauma diagnosis were excluded; this was a claims-level exclusion, not a member-level exclusion. Member months were calculated beginning with the first DOS for chemotherapy and continuing for as long as a member maintained his or her plan eligibility or if no more data were available. Months were included on a fractional basis (ie, number of days included/ number of days in that month), allowing for analysis of partial months.

The per member per month (PMPM) costs (adjusted for disease mix between the HOP and the COC cohorts) were evaluated according to cancer type, geographic location (ie, urban, suburban, rural), patient age, and number of chemotherapy sessions. Other cost categories, including inpatient, outpatient/physician office, or emergency department site of service; chemotherapy drugs; supportive care drugs (eg, anti-emetics, anti-infectives); other drug costs; and other medical expenses were also evaluated according to the setting of chemotherapy drug administration (ie, COC vs HOP). Costs associated with inpatient and/or emergency department sites of service may have reflected treatment- or disease-related complications, adverse reactions to chemotherapy, practice patterns, patient frailty, or other effects.

The Truven Health Analytics MarketScan Database contains the Metropolitan Statistical Area (MSA) in which each member resides. To group patients into urban, suburban, and rural, we used the US Census Bureau’s estimate of the population of the MSA as of July 2011. MSAs with ≥1,000,000 inhabitants were classified as urban, those with >100,000 but <1,000,000 were classified as suburban, and those with ≤100,000 were classified as rural. Any patients living outside of the MSA areas were classified as rural.

Statistical analysis for difference in mean PMPM cost was performed using the Mann-Whitney U test. A linear regression was fitted to determine whether or not a variety of covariates impacted cost, and how much of the total variation was explained by these covariates. The covariates evaluated were cancer type, gender, number of chemotherapy sessions, and geographic location. Covariate analysis included only data available through claims. Differences in populations according to clinical risk profile or severity of illness were not captured in the claims data and therefore could not be analyzed.

The analysis was computed on a PMPM basis to normalize costs. Conditions included 9 common cancer types (Table 1). These types represented approximately 80% of all patients diagnosed with cancer in the database, providing a comprehensive view of spending while avoiding examination of very small patient populations. A total of 70,984 patients were identified and grouped into either the COC or HOP cohorts. Eighty percent (n = 56,649) of patients received their treatment in a COC setting, whereas 20% (n = 14,335) received treatment in a HOP setting.

Gender distribution between the COC and HOP cohorts was similar. However, the mean age in the COC cohort was higher for all disease states, with the largest being a 4.9-year difference for lymphoma patients. All other disease states had mean age differences within 2 years (Table 1).

The COC cohort had an overall mean PMPM cost of $6578—30% lower than the HOP cohort’s overall mean PMPM cost of $9412 (

<.001). This difference was 20% to 39% lower in the COC cohort, depending on diagnosis, and was statistically significant for each cancer type evaluated (

). The cost differential was consistently higher for patients receiving chemotherapy in the HOP cohort across each cancer type (Table 2), regardless of whether a patient was in an urban, suburban, or rural area (data not shown). The cost differential was also consistently higher in the HOP cohort across year of birth (

). Interestingly, the mean number of chemotherapy administrations per member was higher for the COC cohort (16.9) than the HOP cohort (14.6), whereas the mean number of eligible months was lower for the COC cohort (18.2) than the HOP cohort (18.7). All else being equal, we would have expected more administrations in less time to result in higher PMPM chemotherapy treatment cost.

Differences in cost between the COC and HOP cohorts were also evident across numerous cost categories (

); categories examined were: inpatient, outpatient/physician office, and emergency department site of service; chemotherapy drugs; supportive care drugs; other drugs; and other medical expenses. For all cancer types, the PMPM cost for the HOP cohort was higher in inpatient and outpatient/physician office sites of service and chemotherapy drug costs. Eight of 9 cancer types also had a higher PMPM cost for supportive care drugs in the HOP cohort (

All covariates used in the model were statistically significant when used in a multivariate analysis with the exception of prostate cancer patients and patients living in an urban setting. The variables in the model only explain a small amount of the variation in PMPM cost, with R2 = 0.118 and F significance <.001 (Table 4). This means that our site-of-service model is able to explain 12% of the variation in cost from our data alone. The implication is that the variables we incorporated are significant, although other cost determinants exist that should be further evaluated in subsequent studies.

To our knowledge, our study results are the first to show that costs were significantly lower when chemotherapy was administered in a COC compared with an HOP setting and that this cost difference remained significant for each of the 9 cancer types evaluated.

 The COC cohort had a 30% lower mean PMPM cost ($6578) than the HOP’s cohort ($9412). The cost differential was consistently higher in the HOP cohort across cancer type, age, number of chemotherapy sessions, and geographic location. The evaluation of other cancer care-associated cost categories also showed higher costs for the HOP cohort in inpatient and outpatient/physician office sites of service and chemotherapy drug costs across all cancer types evaluated. Despite a higher mean age, increased mean number of chemotherapy administrations per patient, and lower mean number of eligible months for patients in the COC cohort, a higher cost of care was not observed. Because chemotherapy contributes significantly to the overall high cost of cancer treatment, treating patients in a COC setting compared with a HOP setting may potentially result in significant savings in healthcare dollars.

 The results of our study should be interpreted cautiously because the choice of chemotherapy agents, disease stage, or other factors not captured by the database could have significantly influenced the results.

While the limitations of a study based on claims data are recognized, this is the third study using different databases to reach the same conclusion: that cancer care is less expensive in the COC setting.

 Using the fee-for-service population from the Medicare Limited Data Set from 2006 to 2009, the Milliman group reported an approximate $6500 annual cost differential in allowable costs for Medicare patients (ie, all Part A and Part B allowable costs, not just costs associated with chemotherapy or cancer treatment) receiving chemotherapy (n = 79,376) in a COC compared with an HOP setting.

The cost differential was consistently lower in the COC setting across various subgroups, including male gender (any age) and number of chemotherapy sessions (<16 sessions).

A similar analysis by Avalere Health reported similar cost discrepancies between COC and HOP settings in the private health insurance sector. Using a different claims database (n = 22,204 patients) provided by 4 commercial health insurers between 2008 and 2010, Avalere Health concluded there was a 24% chemotherapy cost differential favoring the COC over the HOP setting.

Similar to these studies, our study showed increased costs in the HOP setting across patient age and number of chemotherapy sessions. However, our study also showed an increased cost in the HOP setting regardless of whether a patient was in an urban, suburban, or rural area. The results of our study add further evidence that these costs are generally more expensive in the HOP compared with the COC setting.

Higher costs of care in the HOP setting are likely to be at least partially due to different reimbursement rates for HOP-based versus COC-based oncology clinics.

However, future studies evaluating costs between COC and HOP settings should evaluate other potential clinical variables that may influence costs, including choice of chemotherapy agents (eg, specific regimens), clinical (eg, disease stage), biologic (eg, biomarker status), genetic (eg, breast cancer susceptibility gene mutation status), and other disease-related factors. In addition, any legislative variables that may influence or shift treatment of Medicare patients between HOP and COC settings should be considered. For example, with the implementation of sequestration on March 1, 2013, community-based oncologists have experienced a 2% reduction in Medicare electronic record incentive payments, a 2% reduction in Medicare physician reimbursement, and a decrease in chemotherapy drug reimbursement from average sales price (ASP) + 6% to ASP + 4.3%.

 Two surveys have reported that these reimbursement reductions are leading to shifts in cancer treatment to non-COC settings for Medicare patients.

 Using oncology census data from the American Society of Clinical Oncology and data from the Milliman report, estimates were made by the Community Oncology Alliance on the annualized cost to Medicare of practices sending Medicare cancer patients to an HOP setting for treatment.

They estimated the annualized incremental cost of sending patients to the hospital after 4 weeks of sequestration implementation to be $750,814,000. By estimating that the sequester cut to cancer drugs saves approximately $150,000,000 to Medicare each year, the net impact of the sequester cuts for just cancer drugs is an annualized increase in federal spending of $442,125,000 to $600,816,000.

While the results from our study are likely as far as claims data can go to illuminate the question in cost differential between the HOP and COC settings, this and other study results demonstrate that site of service may have a negative impact on cost. Results from these studies suggest that payers and policy makers should consider site-based cost differentials when developing and negotiating network participation agreements, benefit structures, patient out-of-pocket responsibilities, and reimbursement rates for providers of cancer care. As a society, we struggle with how to improve patient outcomes and reduce costs related to healthcare treatment. A better understanding of which factors contribute to healthcare costs and how they can be altered to achieve the desired goals is needed.

Our study demonstrates that costs were significantly lower in the COC than in the HOP setting. This cost differential was consistent across the clinical, demographic, and geographic variables analyzed. In addition, healthcare costs in the COC cohort were lower than in the HOP cohort across urban, suburban, and rural markets. Further evaluation will be needed to determine whether choice of treatment (eg, specific treatment regimen) or various clinical (eg, disease stage), biologic (eg, biomarker status), and genetic factors impact these differences. Nevertheless, site of service alone is at least 1 major contributor to the cost differences observed in this study. Policy makers and payers should be aware of these differences, as our study suggests that changing trends in sites of service may have an unfavorable impact on cancer spending.

The authors thank Laura Jung and Terri Davidson with Syntaxx Communications, Inc, for writing and editorial assistance, and Ann Morcos of McKesson Specialty Health for editorial comments.

 The US Oncology Network, McKesson Specialty Health (JH, JRH, MEB, DCD, DKV, JG, JLE, JC, RB), The Woodlands, TX; Texas Oncology PA (JRH), Dallas, TX.

 All authors were employed by the US Oncology Network, McKesson Specialty Health, during the conduction and writing of this study. Dr Hoverman is also employed by Texas Oncology. Both Texas Oncology and US Oncology Network, McKesson Specialty Health, are primarily associated with community oncology practices. Mr Hayes has equity interests in McKesson Corporation. Dr Beveridge is currently employed by Humana. Mrs Dilbeck is currently employed by Molecular Health.

 Concept and design (JRH, MEB, DCD, DKV, JLE, RB); acquisition of data (JH, DCD, JC, RB); analysis and interpretation of data (JH, JRH, MEB, JG, JLE, JC, RB); drafting of the manuscript (JH, JRH, MEB, DKV, JG, RB); critical revision of the manuscript for important intellectual content (JH, JRH, MEB, JG, JLE, RB); statistical analysis (JH, RB); provision of patients or study materials (RB); administrative, technical, or logistic support (JLE, RB); supervision (JRH, MEB, DCD, DKV, RB).

Jad Hayes, MS, ASA, MAAA, McKesson Specialty Health, 11000 Westmoor Cir, Ste 125, Westminister, CO 80021. E-mail: 

1. Meropol NJ, Schrag D, Smith TJ, et al; American Society of Clinical Oncology. American Society of Clinical Oncology guidance statement: the cost of cancer care. 

2. Marsland T, Robbins G, Marks A, Cassell R, Philips DG, King K. Reducing cancer costs and improving quality through collaboration with payers: a proposal from the Florida Society of Clinical Oncology. 

3. National Health Expenditure Data. CMS website. 

http://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/NationalHealthAccountsHistorical.html

. Updated December 9, 2014. Accessed May 14, 2013.

4. Kate Fitch, Bruce Pyenson; Milliman, Inc. Cancer patients receiving chemotherapy: opportunities for better management. Milliman website. 

http://us.milliman.com/uploadedFiles/insight/research/health-rr/cancer-patients-receiving-chemotherapy.pdf

. Published March 30, 2010. Accessed May 15, 2013.

5. Dollinger M. Guidelines for hospitalization for chemotherapy. 

6. The Moran Company. Results of analyses for chemotherapy administration utilization and chemotherapy drug utilization, 2005-2011 for Medicare fee-for-service beneficiaries (preliminary results). 

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. Published May 29, 2013. Accessed August 7, 2013.

7. Kate Fitch, Bruce Pyenson; Milliman, Inc. Site of service cost differences for Medicare patients receiving chemotherapy. Milliman website. 

http://us.milliman.com/uploadedFiles/insight/health-published/site-of-service-cost-differences.pdf

. Published October 19, 2011. Accessed April 8, 2013.

8. Avalere Health, LLC. Total cost of cancer care by site of service: physician office vs outpatient hospital. Community Oncology Alliance website. 

http://www.communityoncology.org/pdfs/avalere-cost-ofcancer-care-study.pdf

. Published March 2012. Accessed April 10, 2013.

9. Brow ME, Okon TA, George ER, et al. Thoughts and recommendations on cancer care site of service. 

10. Cavallo J. Sequestration’s impact on cancer care: budget cuts are jeopardizing patient access to care and threating scientific research. 

11. Eagle D, Diaz M. How the sequester cuts are harming oncology. 

12. Frequently asked questions: EHR Incentive Programs (FAQ8173). CMS website. 

https://questions.cms.gov/faq.php?id=5005&faqId=8173

. Published April 18, 2013. Accessed May 19, 2013.

Articles

The cost of care for patients receiving chemotherapy in community oncology clinics is lower than for comparable patients receiving chemotherapy in the hospital outpatient setting.

Cost Differential by Site of Service for Cancer Patients Receiving Chemotherapy

The rising cost of healthcare is a topic of constant discussion and equally constant consternation. A lot of money is spent on healthcare in America, far more than in any other Western country, and over time, there has been tremendous inflation in this spending, particularly when indexed against the GDP.

Oncology seems a particularly prominent passenger on this runaway train. Although oncology spending in both the commercial and Medicare sectors represents less than 10% of the total healthcare spend,

 several big-ticket items bring constant attention to this specialty. And why not, with immunotherapy costing $100,000 per course and oral targeted agents costing $5000 to $10,000 per month? These mind-boggling price tags seem out of reach for the average patient.

A major reason for the concern over the cost of these agents is the perceived lack of value. The clinical trials that have led to the approval of these agents often employ end points, or outcomes, that address only part of the value assessment patients must make. High cost may interfere with living longer or better regardless of disease-free survival or disease stability.

The ability to rank “relative values” associated with our therapeutic choices should empower us to make wise decisions in healthcare. It allows us to maximize value. And to the extent that we can improve outcomes, it allows us to improve quality of care. Since the healthcare dollar has finite limits, objective, evidence-based treatment should help us do better with the dollars available.

Sadly, ample evidence suggests we have far to go. The best marker of how far short we are falling is the regional variability in healthcare spend. Seniors in Minneapolis cost Medicare $3341 per year whereas in Miami, they cost Medicare $8881 per year, 2½ times as much.

 These variations are even more exaggerated when care during the last 6 months of life is analyzed. The data from the Dartmouth Atlas are most compelling.

Utilization of all healthcare services, including hospital days, ICU days, and doctors visits, goes up. But what is most alarming is the variability from state to state and from region to region. For example, hospital days in the last 6 months range from 7 in Utah and Oregon to almost 16 in DC and almost 12 in Florida. Office visits follow the same trends. Not unexpectedly, hospice enrollment follows an inverse relationship. Without question, socioeconomic factors explain some of the variability and there is no clear “quality standard” which allows definition of an appropriate benchmark. However, this much variability cannot be viewed as good.

This type of variability certainly occurs in oncology care, even on a much smaller scale. Our studies looking at charges for lung cancer therapy within a single large practice shows wide interphysician differences.

 Notably, spend was not correlated at all with better survival (

). Patients cared for by the most expensive doctors did just as well (or just as poorly) as those cared for by the most thrifty doctors. In oncology, the spend is spread among several services and even several sites of service. Although there are some publications based on Surveillance, Epidemiology, and End Results data in the Medicare population,

 there was very little in the published literature to guide us in identifying where opportunities lie. We therefore entered into a partnership with Milliman to study costs of cancer care in the commercial population.

The study analyzed data from Medstat 2007, a claims database of commercial insurers, including several BCBS plans, covering 28 million lives. We focused on the top 10 cancer diagnoses, which represented 70,000 individuals. Cancer was relatively uncommon in this cohort, only 0.7% of all covered individuals, but cancer claims made up almost 10% of the cost. The 10 cancer diagnoses represented about 65% of all cancer diagnoses in this cohort, with breast cancer being the most common diagnosis. Among the cancer patients, about 25% received chemotherapy in a given year. The average cost per cancer patient receiving chemotherapy was about $110,000 per year, 4 times as much as the average cancer patient not receiving chemotherapy. Further, the cost for cancer patients receiving chemotherapy was dramatically higher, more than 20-fold greater, than the cost for patients with other chronic diseases like diabetes.

So how is the money spent? The majority of the charges were for outpatient services (

). In analyzing the outpatient costs, only 25% of costs were for chemotherapy. The remainder of the costs included imaging, outpatient surgery, and other ambulatory services. We also found there were about 2 hospitalizations per year and 2 emergency department (ED) visits per year in chemotherapy-treated patients, half of which could be attributed to chemotherapy toxicity. Unfortunately, we found relatively low hospice utilization rates. And more concerning, we found a large cohort of patients who died in the hospital and received chemotherapy within 14 days of death.

As with the published experience with the Dartmouth Atlas, we identified marked regional variations in care (

). The range in chemotherapy costs was almost a third higher in the highutilizing regions, and not surprisingly, hospital admits and ED visits varied 2-fold and 4-fold respectively from lowto high-utilizing regions. This data set identifies opportunities for cost control, especially in chemotherapy costs, hospital and ED costs, and end-of-life costs.

The US Oncology Network has, for the last 6 years, focused on the ability to control costs by adopting an evidence- based treatment platform, Level I Pathways. These evidence-based treatment guidelines are embedded in the iKnowMed EHR and offered as a decision support tool, with reports that allow implementation of a process improvement methodology. We have reported on the impact of these evidence-based pathways in our network in patients with lung cancer and colorectal cancer.

Our first study, in non-small cell lung cancer patients, was a collaboration with Aetna.

 Over an 18-month period, we looked at cost of care in 1400 patients

at 8 sites in the US Oncology Network. Clinical data including stage of disease and line of therapy were documented. We studied chemotherapy costs as well as costs associated with acute care visits, E and M codes, laboratory, and supportive care drugs. Within our sites of service, 1100 of 1400 patients were treated according to guidelines, considered “on pathway,” and 300 of 1400 were treated “off pathway.” There was a substantial cost difference between the groups, with a 35% reduction in chemotherapy costs associated with being treated on pathway ($18,000 vs $27,700). Importantly, survival of those treated on pathway was identical to those treated off pathway (

We also analyzed the effect of pathways compliance in colon cancer.

 In this second study, we looked at costs associated with pathway status (on or off) in a national database with the assistance of Milliman. Since survival data were not available in this claims system, we analyzed survival with respect to pathways status within iKnowMed. We again found a substantial savings associated with being “on pathway,” about 35%. Interestingly, we found evidence that disease-free survival and overall survival with treatment on pathway were at least as good as in the offpathway cohort. A preliminary review of inpatient charges and hospitalization rates within the on pathway/off pathway cohorts suggests reduced costs and utilization in the on-pathway group, providing further evidence of cost savings associated with adherence to these evidence-based treatment guidelines.

This body of data makes a compelling case for value associated with a disciplined, cost-conscious, evidence-based approach to chemotherapy decision making. This value derives from clearly reduced costs but also improved outcomes. The value associated with this approach is physician developed and driven. And a similar physician developed and driven approach may well be feasible to attack costs associated with hospitalizations and ED visits and especially end-of-life care, where there is ample opportunity for improvement.

There are clearly challenges to the broad adoption of this approach. First, there are innumerable evidence-based treatment guidelines. Most of these are at best cost neutral and at worst cost insensitive. It would be a mistake to assume an equal benefit across all of these guidelines. Second, this approach requires a genuine attempt at process improvement. Reporting and accountability are mandatory. Without physician buy-in, the process is doomed to failure. Finally, there is widespread dissatisfaction with the current oncology reimbursement methodology. A transition to a methodology that financially rewards high-level, quantifiable performance will greatly enhance likelihood of general acceptance. There will need to be trust between payers and providers to succeed in this system. Ultimately, the reward will be improved value and enhanced quality.

From New York Oncology Hematology and Albany Cancer Center (MK), Albany NY; Texas Oncology (JRH), Austin, TX; and US Oncology (MK, JRH), Houston, TX.

Drs Kolodziej and Hoverman report employment with US Oncology, Houston, TX.

 Concept and design (MK, JRH); acquisition of data (MK); analysis and interpretation of data (MK, JRH); drafting of the manuscript (MK, JRH); critical revision of the manuscript for important intellectual content (MK, JRH); provision of study materials or patients (MK); and supervision (MK).1. Borger C, Smith S, Truffer C, et al. Health spending projections through 2015: changes on the horizon. Health Aff (Millwood). 2006;25:w61- w73.

2. Tangka FK, Trogdon JG, Richardson LC, Howard D, Sabatino SA, Finkelstein EA. Cancer treatment cost in the United States: has the burden shifted over time? Cancer. 2010;116(14):3477-3484.

3. Congressional Budget Office. Geographic Variation in Healthcare Spending 2005. www.cbo.gov/ftpdocs/89xx/doc8972/2-15-GeogHealth.pdf.

4. The Trustees of Dartmouth College. The Dartmouth Atlas of Health Care. http://www.dartmouthatlas.org/.

5. Hoverman JR, Robertson SM. Lung cancer: a cost and outcome study based on physician practice patterns. Dis Manag. 2004;7:112-123.

6. Hassett MJ, Neville BA, Weeks JC. The relationship between cost, quality, and outcomes among women with breast cancer in SEER-Medicare. J Clin Oncol. 2011;29:384S(suppl; abstr 6001).

7. Kolodziej M, Hoverman JR, Garey JS, et al. Benchmarks for value in cancer care: an analysis of a large commercial population. J Oncol Pract. 2011;7(5):301-306.

8. Neubauer MA, Hoverman JR, Kolodziej M, et al. Cost-effectiveness of evidence-based treatment guidelines for the treatment of non-small-cell lung cancer in the community setting. J Oncol Pract. 2010;6(1):12-18.

9. Hoverman JR, Cartwright TH, Patt DA, et al. Pathways, outcomes, and costs in colon cancer: retrospective evaluations in two distinct databases. J Oncol Pract. 2011;7(3)(suppl 3):52s-59s.

Value-Based Reimbursement in Oncology 

To assess the impact of 2005 and 2006 reductions in chemotherapy reimbursement, mandated in the Medicare Modernization Act, on patterns of chemotherapy receipt in the last 14 days of life.

Included in the study were Medicare beneficiaries dying with poor-prognosis cancer from 2003 to 2007. We compared pre- and postreform probability and frequency of chemotherapy receipt in the last 14 days of life, a validated quality measure, using linear models. We assessed changes in chemotherapy use in physician offices (where prescribing is often directly linked to physician income) and hospital outpatient departments (where the link is indirect and likely weaker).

Among patients receiving chemotherapy in the 6 months before death in physicians’ offices before the policy implementation (2003 to 2004), 18% received chemotherapy in the last 14 days of life. Those dying after implementation (2006 to 2007) were 3.5 percentage points (95% confidence interval [CI], —5.4 to –1.6; P <.001), or 20%, less likely to receive chemotherapy in the 14 days before death than those dying before implementation. By contrast, there was no significant change in the percentage of patients receiving chemotherapy in the last 14 days of life in hospital outpatient departments between 2003 and 2004 and between 2006 and 2007.

 In physician offices, where drugs generate the majority of revenue, and prescribing patterns can determine physician income, use of chemotherapy at the end of life fell significantly after reimbursement reductions; no concurrent change occurred in hospital outpatient departments. These results suggest that payment reform may be used to better align appropriate financial incentives with better quality of care.

(Am J Manag Care. 2012;18(5 Spec No. 2):e200-e208)

In the early 2000s, Medicare drug reimbursements provided high margins under an average wholesale price system, which permitted physicians to obtain drugs well below reimbursed rates.

 In response to concerns about rapid growth in the use of chemotherapy drugs with high margins, the Medicare Modernization Act (MMA) changed reimbursements to more closely reflect acquisition costs.

 The MMA reduced payments to 106% of manufacturer-reported average sales prices while increasing drug administration fees; the change took effect in physician offices in 2005 and in hospital outpatient departments in 2006. The Government Accountability Office estimated that the MMA reform would reduce the average profit margin on infused chemotherapy drugs from 22% to 6%.

 Payments for chemotherapy and anemia medications dropped by $1.2 billion between 2004 and 2005, after implementation of the reform in physicians’ offices.

 This payment change had a large impact on oncologist practice revenue, because drug reimbursement accounted for 77% and drug administration accounted for approximately 10% of practice revenue during this time period.

Policy makers have noted the potential conflicts of interest inherent in the pre-MMA system of payment for chemotherapy treatments.

 Studies assessing the effect of MMA reimbursement changes on access to and use of chemotherapy in patients with newly diagnosed cancer have generated mixed results.

 Despite fears that physicians would cease to offer chemotherapy services in their offices, restricting access to care and increasing volume in hospital outpatient departments,

2 studies found no meaningful effects on access to chemotherapy as measured by wait time, travel time, or infusion locale.

Using Medicare data 6 and 10 months into the payment change, respectively, the Medicare Payment Advisory Commission

 found increased use of chemotherapy in patients with newly diagnosed cancer and evidence of switching to more costly agents. However, these studies focused on newly diagnosed patients cared for in a short window after MMA implementation and provide little sense of how changes affected quality of care.

The effect of financial incentives on service delivery may be most pronounced in cases of marginal or low value,

 such as chemotherapy at the end of life, where aggressive anticancer therapies may prove to be more toxic than beneficial, use is not related to probability of benefit, and appropriately timed cessation of chemotherapy is integral to a patient’s terminal quality of life.

We augment evidence on the impact of payment reform for Part B drugs and provider response to financial incentives through a study of end-of-life chemotherapy treatment.

 We expand current understanding in 3 ways. We examined a measure of the quality of cancer care—chemotherapy receipt in the last 14 days of life, a validated quality measure and now included as a benchmark for improving clinical practice in the Quality Oncology Practice Initiative (QOPI).

 Second, we examined the effects of payment changes in physician office settings separately from hospital outpatient department settings to leverage differences in financial incentives. In office settings, chemotherapy use is often directly related to physician income and makes up a large portion of practice revenue; in the hospital outpatient setting, drug reimbursement makes up a much smaller proportion of the revenue mix, and the link between physician income and prescribing patterns is indirect and likely weaker. We hypothesized that changes in chemotherapy payment were more likely to alter prescribing decisions in physicians’ offices; modeling the groups separately allowed us to disentangle changes in behavior as a result of reimbursement from broader trends in end-of-life care. Finally, by examining data through 2007, we were able to observe effects of the policy change that occurred over a longer time horizon compared with previous studies and thus did not limit our assessment to immediate policy response.

We used Medicare claims data for beneficiaries dying with poor-prognosis cancer from 2003 to 2007 and assessed pre- (2003 to 2004) and post- (2006 to 2007) reform probability and frequency of chemotherapy receipt in the last 14 days and 3 months of life. We analyzed trends in treatment before and after reform by treatment location (physician offices and hospital outpatient departments).

From the 20% Medicare denominator files spanning 2003 to 2007, we identified fee-for-service Medicare beneficiaries who died between age 66 and 99 years and had continuous Parts A and B coverage in the last 6 months of life. Decedents were included in the study if they had at least 1 hospital claim or at least 2 clinician visits in the last 6 months of life with poor-prognosis cancer.

 Poor-prognosis cancer was defined by Iezzoni et al

 using claim diagnosis codes associated with high rates of death during hospital admissions, thus permitting us to create a cohort for whom providers likely understood prognosis to be poor in the last 6 months of life. We categorized decedents into 1 of 26 cancer types based on their predominant cancer diagnosis.

For each patient, we used billing codes to assess receipt of outpatient chemotherapy (administered by a clinician or facility) in the last 6 months of life, last 3 months of life, and last 14 days of life (codes defined in the 

). Our main outcome of interest was receipt of chemotherapy in the last 14 days of life, conditional on receipt in the last 6 months of life. We chose this measure because it is similar to the quality metric defined by Earle et al.

We determined setting of chemotherapy infusion based on claim file type and place of service code during the last 6 months of life (hospital outpatient department, physician office, or both). We also assessed the number of chemotherapy treatments in each time window among those who received chemotherapy, defined as the number of days of treatment.

From Medicare files, we obtained patient age at death, race (dichotomized as black or nonblack), state of residence, and sex. We used the methods of Iezzoni et al

to assign patients up to 8 noncancer chronic conditions based on 1 inpatient or 2 outpatient

 International Classification of Diseases, Ninth Revision

, diagnosis claims occurring between 6 months and 1 month before death (

 We categorized patients’ metastatic cancer status using the definitions of Iezzoni et al.

 Each patient’s residential zip code was used to assign an estimated household income based on US Census tract data and the proportion of the population in that area in poverty.

 Models included these patient characteristics as well as cancer type and age squared.

We conducted 2 sets of analyses. We modeled the distinct individual monthly impacts of the 2005 and 2006 policy implementations and then considered their impact collectively as a single payment change, implementation of which spanned 12 months.

We plotted regression-adjusted mean predicted probabilities that a patient received chemotherapy in the last 2 weeks of life and last 3 months of life by setting of treatment (Fig 1). We estimated ordinary least squares models of the probability of chemotherapy receipt or frequency of chemotherapy receipt as a function of payment change, where the patient was the unit of analysis. We captured payment change with a linear time trend with coefficients to capture changes in the time trend in January 2005 and January 2006. We chose the linear time trend because it fit the raw data accurately and is readily interpretable. In sensitivity tests of the specification, we obtained similar results estimating probability of chemotherapy receipt using logistic regression. All regression models controlled for a linear time trend to capture trends in end-of-life treatment during the full time period. We estimated models for the entire cohort and separate models by site of chemotherapy receipt in the last 6 months (physician office or outpatient department). We also predicted each outcome without payment reform, assuming the time trend from the period before the payment change (2003 to 2004) persisted (ie, as if changes in trend in January 2005 and January 2006 were zero).

We repeated these models after dropping patients who died in 2005 and estimated the cumulative effect of the payment change in January 2005 (physician offices) and January 2006 (hospital outpatient departments) as a single event, controlling for a linear time trend and the same covariates. This model has 2 advantages. It estimates the magnitude of the overall effect of the MMA on probability and frequency of chemotherapy receipt, allowing for a transition period as the payment change in physicians’ offices had been in place for 1 year. Second, it excludes the cohort whose last 6 months before death spanned the pre-2005 and post-2005 periods, yielding a cleaner definition of the pre- and postperiod observations. We adjusted variance estimates for the correlation of observations within state and time period (before January 2005, after December 2005) using Huber-White sandwich estimators. 

Because previous publications studying effects of the MMA have focused specifically on incident lung cancer,

 we repeated our analysis for the subset of patients with lung cancer.

Overall, 235,821 patients met inclusion criteria; 32% received chemotherapy in the last 6 months of life (n = 75,026; Table 1). Mean age at death was 78.1 years; 49% were women; 9% were black. The most common cancer types were: lung cancer (32%), cancer of unknown primary (10%), and hematologic cancer (9%). Among patients who received chemotherapy within 6 months of death, 74% received all chemotherapy treatments in a physician’s office, 21% received all treatments in a hospital outpatient department, and 5% received treatment in both places. Receipt of chemotherapy in hospital outpatient departments increased over the time period (from 18% in 2003 to 25% in 2007). Demographics varied by setting of administration, with those receiving chemotherapy in a physician’s office significantly more likely to be older, nonblack, male, and from a wealthier zip code. Those with more comorbidities were treated in physicians’ offices. Setting-specific population characteristics were similar among the subset of patients with lung cancer, although these patients were more likely to be younger, nonblack, and poorer and have more comorbidities relative to the full sample.

Among those receiving chemotherapy in the last 6 months of life, 77% received chemotherapy in the last 3 months of life, and 15% received chemotherapy in the last 14 days of life over the full 2003 to 2007 time period (Table 1). Overall, chemotherapy receipt near the end of life was significantly more likely for those treated in physician office settings versus hospital outpatient departments. Of those treated in physician offices, 77% received chemotherapy in the last 3 months of life, and those who received chemotherapy in this window averaged 4.1 days of treatment. In physicians’ offices, 16% received chemotherapy in the last 14 days of life and averaged 1.5 days of treatment. Of those using outpatient departments for treatment, 73% received chemotherapy in the last 3 months of life (averaging 3.4 days of treatment), and 12% received chemotherapy in the last 14 days (averaging 1.4 days of treatment). All of these differences across settings are statistically significant, with 

 values below .05. Patterns were similar among the subset of patients with lung cancer.

graphically presents results from models considering the 2 policy implementation events separately. It indicates that within both time windows (14 days and 3 months) before death, the probability of chemotherapy receipt dropped significantly after the payment change in physician offices, whereas it increased slightly in hospital outpatient departments. Patterns were similar among those with lung cancer.

 lists the coefficients on the payment change when transitional data from 2005 are dropped, and the payment change is measured as a one-time shift, controlling for patient demographics, comorbidities, cancer type, metastatic disease, and a linear time trend. Comparing 2006 and 2007 with 2003 and 2004, the rate of chemotherapy receipt in the last 14 days of life fell 2.6 percentage points (or 2.6 per 100 patients), a drop of approximately 20% (95% confidence interval [CI], —4.2 to –1.0; 

 = .002). The drop was driven entirely by reductions in terminal chemotherapy in physician offices, where the probability of chemotherapy in the last 14 days dropped 3.5 per 100 patients (95% CI, —5.4 to –1.6; 

<.001) relative to the mean of 18% in the preperiod.For those who received treatment in a hospital outpatient department, there was no significant change over the same time period (95% CI, —2.4 to 4.4; 

 = .541). We also observed significant reductions in chemotherapy for the last 6 months of life (2.5 per 100 patients; 95% CI, —3.7 to –1.3; P <.001) and the last 3 months of life (4.2 per 100 patients; 95% CI, –6.1 to –2.2; 

 <.001). In the lung cancer cohort, there were no significant changes in chemotherapy in the last 14 days, but estimates of changes in probability in the last 3 and 6 months were similar in significance and magnitude to the full cohort. Because the composition of cancer type varied across the 2 settings, and differences in treatment for lung cancer (such as epidermal growth factor receptor inhibitors as a later line of therapy) could be a possible confounder, we estimated a model excluding patients with lung cancer. The results were unchanged.

In the full cohort who received chemotherapy during the window, the number of treatments per patient declined by 0.95 treatments in the last 6 months (95% CI, —1.3 to –0.6; 

 <.001) and 0.61 treatments in the last 3 months (95% CI, —0.8 to –0.4; 

 <.001). Taking into account the reductions in probability of receipt and frequency of receipt after the payment change in this cohort of patients with poor-prognosis cancers, we estimate that during a 2-year period, there were 546,000 fewer chemotherapy treatments in the last 6 months of life, and the number of patients treated in the last 14 days fell by 7900. Using the postreform 2006 average payment per chemotherapy treatment ($720) leads to an estimated additional cost savings (through the reduction in quantity) of roughly $400 million.

The MMA entailed sharp cutbacks in chemotherapy payments from 2005 to 2006. In this study, we found that chemotherapy receipt in the last 14 days of life fell 20% for patients treated in physicians’ offices after the MMA legislation was implemented, with no corresponding decline among those treated in hospital outpatient departments. Previous articles have examined responses to the MMA in use of chemotherapy in newly diagnosed patients but have provided little evidence to determine whether treatment changes improved or degraded quality of care.

Near the end of life, aggressive anticancer therapies may prove to be more toxic than beneficial, their use may not be related to probability of providing benefit, and appropriately timed cessation of chemotherapy is integral to a patient’s terminal quality of life.

Can chemotherapy in the last 14 days of life be used as an outcome measure? Clearly, the right rate of treatment in the last 14 days is not zero, because some deaths, even for those with metastatic cancer where chemotherapy is appropriate, occur unexpectedly. Yet chemotherapy in the last 14 days is a well-established and validated quality measure.

 Furthermore, there is little reason why the unanticipated mortality rate for patients being treated with chemotherapy should have declined between 2003 and 2004 and between 2006 and 2007 only in physicians’ offices and not in hospital outpatient departments.

Why are the effects of payment reform so much greater in physicians’ offices compared with hospital outpatient departments? One reason may be that in hospital outpatient departments, physicians have no direct incentive to order chemotherapy of marginal benefit, and the fraction of total revenue composed of drug reimbursement is small. If the decrease in the use of end-of-life chemotherapy at this time were driven by broader trends such as diffusion of quality standards by oncology groups, the increasing acceptance of palliative care,

 or the introduction of new drugs, we would expect to see equivalent changes in both settings. Unfortunately, we cannot observe the differences between practices in treatment of drug revenue; some practices decouple personal income from chemotherapy use. This should also be considered as a policy option.

Payment reform may be used to better align appropriate financial incentives with better quality of care.

Impact of Payment Reform on Chemotherapy at the End of Life

This article was published as part of a special joint issue and also appears in the 

The goal of this study was to use 2 separate databases to evaluate the clinical outcomes and the economic impact of adherence to Level I Pathways, an evidence-based oncology treatment program in the treatment of colon cancer.

 The first study used clinical records from an electronic health record (EHR) database to evaluate survival according to pathway status in patients with colon cancer. Disease-free survival in patients receiving adjuvant treatment and overall survival in patients receiving first-line therapy for metastatic disease was calculated. The second study used claims data from a national administrative claims database to examine direct medical costs and use, including the cost of chemotherapy and of chemotherapy-related hospitalizations according to pathway status.

Overall costs from the national claims database—including total cost per case and chemotherapy costs&mdash;were lower for patients treated according to Level I Pathways (on- Pathway) compared with patients not treated according to Level I Pathways. Use of pathways was also associated with a shorter duration of therapy and lower rate of chemotherapy-related hospital admissions. Survival for patients on- Pathways in the EHR database was comparable with that in the published literature.

Results from 2 distinct databases suggest that treatment of patients with colon cancer on-Pathways costs less; use of these pathways demonstrates clinical outcomes consistent with published evidence.

(Am J Manag Care. 2011;17(5 Spec No.):SP45-SP52) 

	This report assesses the impact of adherence to evidence-based treatment pathways on clinical outcomes, treatment complications, and cost of care in colon cancer.

		This information can be used in practice and policy decisions when evaluating programs to address quality and value in cancer care, including the different data sources and the strengths and limitations of clinical and claims information.

Colorectal cancer (CRC) is the fourth most commonly diagnosed cancer in the United States and the second leading cause of cancer-related deaths.

 Survival has improved as a result of advancements in medical therapy and the availability of new treatments.

 The introduction of new agents has resulted in increased variability in treatment options and increased costs.

 or clinical pathways, have been developed to standardize treatment and improve quality of care. Little information exists, however, regarding the impact of adherence to pathways on clinical outcomes, treatment complications, and cost of care. Data suggest that the use of clinical pathways in oncology can produce improvements in some areas—including length of hospital stay, complications, and financial outcomes

—and are cost-effective in treating non-smallcell lung cancer.

Level I Pathways is a set of treatment guidelines established as the foundation of an evidence-based oncology treatment program developed and led by physicians in the US Oncology (The Woodlands, TX) network. It uses only high-level evidence to assess efficacy, toxicity, and cost when recommending therapies. Treatments are updated regularly by a multidisciplinary task force in collaboration with disease research committees and practicing community oncologists. Recommendations are based on lines of therapy and are integrated into the iKnowMed (iKM) electronic health records (EHR) system to provide point-of-care decision support.

The MedStat MarketScan database (Thomson Reuters, New York, NY) represents the inpatient and outpatient healthcare service use of individuals nationwide covered by the benefit plans of large employers, health plans, and government and public organizations. The MarketScan database links paid claims and encounter data to detailed patient information across sites and types of providers over time. The annual medical database includes private sector health data from approximately 100 payers.

Our objective was to conduct 2 separate studies by using the 2 databases to evaluate the clinical outcomes and the economic impact of adherence to Level I Pathways in colon cancer treatment. The first study used clinical records from the iKM EHR database to evaluate survival. To address concern that pathways use may adversely impact quality of care in this study, we examined survival according to pathway status. In the second study, we applied a set of pathway rules to a separate cohort of patients with colon cancer from the MedStat national claims database to examine the cost of chemotherapy and chemotherapy-related adverse events.

Retrospective evaluations of electronic health records and claims databases to assess clinical outcomes and costs associated with evidence-based pathways in colon cancer.

J. Russell Hoverman, MD, PhD

Articles

Cost Differential by Site of Service for Cancer Patients Receiving Chemotherapy

Value-Based Reimbursement in Oncology

Impact of Payment Reform on Chemotherapy at the End of Life

Pathways, Outcomes, and Costs in Colon Cancer: Retrospective Evaluations in 2 Distinct Databases